The present invention relates generally to the field of medical imaging analysis. Particularly, the present invention relates to a method and system for candidates selection of mass density from digital mammography images in conjunction with computer-aided detection, review and diagnosis (CAD) for mammography CAD server and digital mammography workstation.
The U.S. patent Classification Definitions: 382/254 (class 382, Image Analysis, subclass 254 Image Enhancement or Restoration); 382/128 (class 382, Image Analysis, subclass 128 Biomedical applications).
Mass density candidates are the locations on mammograms that are used as initial regions of interest to detect potential breast cancers that present abnormal signs of mass densities or architectural distortions. Most existing candidate selection algorithms are based on the intensity of the images, such as, a combination of the global maximum and local maximum (see U.S. Pat. No. 5,615,243 issued in March, 1997, to Chang et al. entitled “Identification of suspicious mass regions in mammograms”), multi-gray-level thresholding on a subtracted image (see U.S. Pat. No. 5,832,103 issued in November, 1998, to Giger et al. entitled “Automated method and system for improved computerized detection and classification of masses in mammograms”), peak selection from multiple Fourier band-pass images (see U.S. Pat. No. 6,246,782 issued in June, 2001, to Shapiro et al. entitled “System for automated detection of cancerous masses in mammograms”). Using a limited discrete number of levels or bands to select the mass densities, which have a continuous range of intensity levels and sizes, requires ad hoc adjustment of a large number of parameters. Intensity-based methods also usually perform calculations on multiple images, which results in expensive computation.
Accordingly, a method of selecting mass density candidates from a digital image for computer-aided cancer detection, review and diagnosis includes down-sampling the digital image to a low resolution; smoothing an edge along a skinline; applying a Gaussian difference filter to enhance intensity to form a filtered image; masking the filtered image using a breast mask; using a Canny detector to find potential mass density contours; and generating a mass density candidate list from Canny contours produced in the Canny detector.